差异化需求下的非关系型分布式报送信息大数据分类方法

doi:10.11959/j.issn.1000-0801.2023122

Abstract

Abstract:

The classification method of non-relational distributed submitted information big data under the differentiated demand was studied, aiming at the problem of multi-source heterogeneous, widely distributed submitted information with more differentiated application requirements and inability to distinguish the available information.Firstly, the usability, openness and expansibility of the non-relational distributed submission information database were analyzed.The unstructured database storage TRIP was used to store non-relational distributed submission information by combining the basic requirements of field types.Then, the hashing process within the Hamming hash family was analyzed.Under the constraint of linearity level requirements, cellular automata with multiple attractors were used to optimize the system.The optimal parameters of the multiple attractor cellular automata classifier were improved through genetic algorithm, thus improving the big data classification method.Experimental results show that this method can effectively identify and classify structured data and unstructured data in non relational distributed submission information, and has high classification accuracy.

Key words: differentiated demand, non relational, distributed, submit information, big data classification, cellular automata

CLC Number:

TP311

Lu HAN, Weiyu CHEN, Fei ZHANG, Jianfeng HE, Huaizhen SU. Big data classification method of non relational distributed submission information under differentiated requirements[J]. Telecommunications Science, 2023, 39(6): 114-121.

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References 15

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参数优化前
报送信息量/byte	5 000		10 000
输入层与隐藏层比特维数比值	100:5	300:10	100:5	300:10
分类精度	86.81%	87.24%	85.93%	86.10%
内存消耗/byte	279	608	279	608
参数优化后
报送信息量/byte	5 000		10 000
输入层与隐藏层比特维数比值	100:5	300:10	100:5	300:10
分类精度	96.73%	95.56%	96.01%	96.22%
内存消耗/byte	186	391	186	391

Big data classification method of non relational distributed submission information under differentiated requirements

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